2001

In Humanoids2001, Second IEEE-RAS International Conference on Humanoid Robots, 2001, clmc (inproceedings)

Abstract

Oculomotor control in a humanoid robot faces similar problems as biological oculomotor systems, i.e., the stabilization of gaze in face of unknown perturbations of the body, selective attention, the complexity of stereo vision and dealing with large information processing delays. In this paper, we suggest control circuits to realize three of the most basic oculomotor behaviors - the vestibulo-ocular and optokinetic reflex (VOR-OKR) for gaze stabilization, smooth pursuit for tracking moving objects, and saccades for overt visual attention. Each of these behaviors was derived from inspirations from computational neuroscience, which proves to be a viable strategy to explore novel control mechanisms for humanoid robotics. Our implementations on a humanoid robot demonstrate good performance of the oculomotor behaviors that appears natural and human-like.

This article explores a new approach to learning by imitation and trajectory formation by representing movements as mixtures of nonlinear differential equations with well-defined attractor dynamics. An observed movement is approximated by finding a best fit of the mixture model to its data by a recursive least squares regression technique. In contrast to non-autonomous movement representations like splines, the resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy which is robust to strong external perturbations and that can be modified by additional perceptual variables. This movement policy remains the same for a given target, regardless of the initial conditions, and can easily be re-used for new targets. We evaluate the trajectory formation system (TFS) in the context of a humanoid robot simulation that is part of the Virtual Trainer (VT) project, which aims at supervising rehabilitation exercises in stroke-patients. A typical rehabilitation exercise was collected with a Sarcos Sensuit, a device to record joint angular movement from human subjects, and approximated and reproduced with our imitation techniques. Our results demonstrate that multi-joint human movements can be encoded successfully, and that this system allows robust modifications of the movement policy through external variables.

Real-time modeling of complex nonlinear dynamic processes has become increasingly important in various areas of robotics and human augmentation. To address such problems, we have been developing special statistical learning methods that meet the demands of on-line learning, in particular the need for low computational complexity, rapid learning, and scalability to high-dimensional spaces. In this paper, we introduce a novel algorithm that possesses all the necessary properties by combining methods from probabilistic and nonparametric learning. We demonstrate the applicability of our methods for three different applications in humanoid robotics, i.e., the on-line learning of a full-body inverse dynamics model, an inverse kinematics model, and imitation learning. The latter application will also introduce a novel method to shape attractor landscapes of dynamical system by means of statis-tical learning.

We present a model, composed of hierarchy of artificial neural networks, for robot learning by demonstration. The model is implemented in a dynamic simulation of a 41 degrees of freedom humanoid for reproducing 3D human motion of the arm. Results show that the model requires few information about the desired trajectory and learns on-line the relevant features of movement. It can generalize across a small set of data to produce a qualitatively good reproduction of the demonstrated trajectory. Finally, it is shown that reproduction of the trajectory after learning is robust against perturbations.

Sensory-motor integration is one of the key issues in robotics. In this paper, we propose an approach to rhythmic arm movement control that is synchronized with an external signal based on exploiting a simple neural oscillator network. Trajectory generation by the neural oscillator is a biologically inspired method that can allow us to generate a smooth and continuous trajectory. The parameter tuning of the oscillators is used to generate a synchronized movement with wide intervals. We adopted the method for the drumming task as an example task. By using this method, the robot can realize synchronized drumming with wide drumming intervals in real time. The paper also shows the experimental results of drumming by a humanoid robot.

The 2/3 power law, the nonlinear relationship between tangential velocity and radius of curvature of the endeffector trajectory, has been suggested as a fundamental constraint of the central nervous system in the formation of rhythmic endpoint trajectories. However, studies on the 2/3 power law have largely been confined to planar drawing patterns of relatively small size. With the hypothesis that this strategy overlooks nonlinear effects that are constitutive in movement generation, the present experiments tested the validity of the power law in elliptical patterns which were not confined to a planar surface and which were performed by the unconstrained 7-DOF arm with significant variations in pattern size and workspace orientation. Data were recorded from five human subjects where the seven joint angles and the endpoint trajectories were analyzed. Additionally, an anthropomorphic 7-DOF robot arm served as a "control subject" whose endpoint trajectories were generated on the basis of the human joint angle data, modeled as simple harmonic oscillations. Analyses of the endpoint trajectories demonstrate that the power law is systematically violated with increasing pattern size, in both exponent and the goodness of fit. The origins of these violations can be explained analytically based on smooth rhythmic trajectory formation and the kinematic structure of the human arm. We conclude that in unconstrained rhythmic movements, the power law seems to be a by-product of a movement system that favors smooth trajectories, and that it is unlikely to serve as a primary movement generating principle. Our data rather suggests that subjects employed smooth oscillatory pattern generators in joint space to realize the required movement patterns.

Labeled Graph Matching (LGM) has been shown successful in numerous ob-ject vision tasks. This method is the basis for arguably the best face recognition system in the world. We present an algorithm for visual pattern recognition that is an extension of LGM ("LGM+"). We compare the performance of LGM and LGM+ algorithms with a state of the art statistical method based on Mutual Information Maximization (MIM). We present an adaptation of the MIM method for multi-dimensional Gabor wavelet features. The three pattern recognition methods were evaluated on an object detection task, using a set of stimuli on which none of the methods had been tested previously. The results indicate that while the performance of the MIM method operating upon Gabor wavelets is superior to the same method operating on pixels and to LGM, it is surpassed by LGM+. LGM+ offers a significant improvement in performance over LGM without losing LGMâ??s virtues of simplicity, biological plausibility, and a computational cost that is 2-3 orders of magnitude lower than that of the MIM algorithm.Â

Oculomotor control in a humanoid robot faces similar problems as biological oculomotor systems, i.e. the stabilization of gaze in face of unknown perturbations of the body, selective attention, stereo vision, and dealing with large information processing delays. Given the nonlinearities of the geometry of binocular vision as well as the possible nonlinearities of the oculomotor plant, it is desirable to accomplish accurate control of these behaviors through learning approaches. This paper develops a learning control system for the phylogenetically oldest behaviors of oculomotor control, the stabilization reflexes of gaze. In a step-wise procedure, we demonstrate how control theoretic reasonable choices of control components result in an oculomotor control system that resembles the known functional anatomy of the primate oculomotor system. The core of the learning system is derived from the biologically inspired principle of feedback-error learning combined with a state-of-the-art non-parametric statistical learning network. With this circuitry, we demonstrate that our humanoid robot is able to acquire high performance visual stabilization reflexes after about 40 s of learning despite significant nonlinearities and processing delays in the system.

Rhythmically bouncing a ball with a racket was investigated and modeled with a nonlinear map. Model analyses provided a variable defining a dynamically stable solution that obviates computationally expensive corrections. Three experiments evaluated whether dynamic stability is optimized and what perceptual support is necessary for stable behavior. Two hypotheses were tested: (a) Performance is stable if racket acceleration is negative at impact, and (b) variability is lowest at an impact acceleration between -4 and -1 m/s2. In Experiment 1 participants performed the task, eyes open or closed, bouncing a ball confined to a 1-dimensional trajectory. Experiment 2 eliminated constraints on racket and ball trajectory. Experiment 3 excluded visual or haptic information. Movements were performed with negative racket accelerations in the range of highest stability. Performance with eyes closed was more variable, leaving acceleration unaffected. With haptic information, performance was more stable than with visual information alone.

The goal of our research is to investigate the interplay between oculomotor control, visual processing, and limb control in humans and primates by exploring the computational issues of these processes with a biologically inspired artificial oculomotor system on an anthropomorphic robot. In this paper, we investigate the computational mechanisms for visual attention in such a system. Stimuli in the environment excite a dynamical neural network that implements a saliency map, i.e., a winner-take-all competition between stimuli while simultenously smoothing out noise and suppressing irrelevant inputs. In real-time, this system computes new targets for the shift of gaze, executed by the head-eye system of the robot. The redundant degrees-of- freedom of the head-eye system are resolved through a learned inverse kinematics with optimization criterion. We also address important issues how to ensure that the coordinate system of the saliency map remains correct after movement of the robot. The presented attention system is built on principled modules and generally applicable for any sensory modality.

Real-time control of the endeffector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates learning of inverse kinematics for resolved motion rate control (RMRC) employing an optimization criterion to resolve kinematic redundancies. Our learning approach is based on the key observations that learning an inverse of a non uniquely invertible function can be accomplished by augmenting the input representation to the inverse model and by using a spatially localized learning approach. We apply this strategy to inverse kinematics learning and demonstrate how a recently developed statistical learning algorithm, Locally Weighted Projection Regression, allows efficient learning of inverse kinematic mappings in an incremental fashion even when input spaces become rather high dimensional. The resulting performance of the inverse kinematics is comparable to Liegeois ([1]) analytical pseudo inverse with optimization. Our results are illustrated with a 30 degree-of-freedom humanoid robot.

Following a moving target with a narrow-view foveal vision system is one of the essential oculomotor behaviors of humans and humanoids. This oculomotor behavior, called ``Smooth Pursuit'', requires accurate tracking control which cannot be achieved by a simple visual negative feedback controller due to the significant delays in visual information processing. In this paper, we present a biologically inspired and control theoretically sound smooth pursuit controller consisting of two cascaded subsystems. One is an inverse model controller for the oculomotor system, and the other is a learning controller for the dynamics of the visual target. The latter controller learns how to predict the target's motion in head coordinates such that tracking performance can be improved. We investigate our smooth pursuit system in simulations and experiments on a humanoid robot. By using a fast on-line statistical learning network, our humanoid oculomotor system is able to acquire high performance smooth pursuit after about 5 seconds of learning despite significant processing delays in the syste

Oculomotor control in a humanoid robot faces similar problems as biological oculomotor systems, i.e., capturing targets accurately on a very narrow fovea, dealing with large delays in the control system, the stabilization of gaze in face of unknown perturbations of the body, selective attention, and the complexity of stereo vision. In this paper, we suggest control circuits to realize three of the most basic oculomotor behaviors and their integration - the vestibulo-ocular and optokinetic reflex (VOR-OKR) for gaze stabilization, smooth pursuit for tracking moving objects, and saccades for overt visual attention. Each of these behaviors and the mechanism for their integration was derived with inspiration from computational theories as well as behavioral and physiological data in neuroscience. Our implementations on a humanoid robot demonstrate good performance of the oculomotor behaviors, which proves to be a viable strategy to explore novel control mechanisms for humanoid robotics. Conversely, insights gained from our models have been able to directly influence views and provide new directions for computational neuroscience research.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems